What Happens when K Changes?

Source

• What are the concerns with choosing the optimal K?
• What do you notice about the two choices above?
• Put your observations in the chat

Module completion checklist

GridSearchCV for optimal hyperparameters

Finding optimal k

• We will now use the GridSearchCV function to find the optimal number of k
• As you walk through the steps, keep in mind that this is a method that we can use to tune various hyperparameters depending on the model
• Let’s look at the steps below:

1. Define the hyperparameter value (in this case, we are defining the range of k)
2. Create a hyperparameter grid that maps the values to be searched
3. Fit the grid with data from the model
4. View the scores and choose the optimal hyperparameter
5. View the hyperparameters of the “best model”

Finding optimal k - GridSearchCV

# Define the parameter values that should be searched.
k_range = list(range(1, 31))

# Create a parameter grid: map the parameter names to the values that should be searched by building a Python dictionary.
# key: parameter name
# value: list of values that should be searched for that parameter
# single key-value pair for param_grid
param_grid = dict(n_neighbors = k_range)
print(param_grid)

# Instantiate the grid using our original model - kNN with k.

{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]}

grid = GridSearchCV(kNN, param_grid, cv = 10, scoring = 'accuracy')

Finding optimal k - GridSearchCV

• Again, we need to make sure to scale before our gridsearch

# Create a pipeline of the scaler and gridsearch
grid_search_pipeline = Pipeline([('transformer',  StandardScaler()), ('estimator', grid)])

# Fit Gridsearch pipeline
grid_search_pipeline.fit(X, y)

Pipeline(steps=[('transformer', StandardScaler()),
                ('estimator',
                 GridSearchCV(cv=10, estimator=KNeighborsClassifier(),
                              param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7,
                                                          8, 9, 10, 11, 12, 13,
                                                          14, 15, 16, 17, 18,
                                                          19, 20, 21, 22, 23,
                                                          24, 25, 26, 27, 28,
                                                          29, 30]},
                              scoring='accuracy'))])

Finding optimal k - view results

# View the complete results (list of named tuples).
print(grid.cv_results_['mean_test_score'])

[0.91643836 0.94637965 0.94168297 0.94990215 0.9481409  0.95009785
 0.94951076 0.95088063 0.95029354 0.95107632 0.95068493 0.95088063
 0.95068493 0.95107632 0.95107632 0.95107632 0.95107632 0.95127202
 0.95127202 0.95127202 0.95127202 0.95127202 0.95127202 0.95127202
 0.95127202 0.95127202 0.95127202 0.95127202 0.95127202 0.95127202]

Finding optimal k

• First, we create a list of mean scores

# Create a list of the mean scores only by using a list comprehension to loop through grid.cv_results_.
grid_mean_scores = [result for result in grid.cv_results_['mean_test_score']]
print(grid_mean_scores)

[0.9164383561643836, 0.9463796477495107, 0.941682974559687, 0.9499021526418787, 0.9481409001956946, 0.9500978473581212, 0.9495107632093933, 0.950880626223092, 0.9502935420743638, 0.9510763209393346, 0.9506849315068493, 0.950880626223092, 0.9506849315068493, 0.9510763209393346, 0.9510763209393346, 0.9510763209393346, 0.9510763209393346, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773, 0.9512720156555773]

Finding optimal k - plot

# Plot the results.
_ = plt.plot(k_range, grid_mean_scores)
_ = plt.xlabel('Value of K for kNN')
_ = plt.ylabel('Cross-Validated Accuracy')
plt.show()

Define and examine the optimized model

# Single best score achieved across all params (k).
print(grid.best_score_)

0.9512720156555773

grid_score = grid.best_score_

# Dictionary containing the parameters (k) used to generate that score.
print(grid.best_params_)

# Actual model object fit with those best parameters.
# Shows default parameters that we did not specify.

{'n_neighbors': 18}

print(grid.best_estimator_)

KNeighborsClassifier(n_neighbors=18)

Add GridSearchCV score to the final scores

• So now that we have it, let’s add this score to the dataframe we created earlier

model_final = model_final.append({'metrics' : "accuracy" , 
                                  'values' : round(grid_score, 4),
                                  'model':'kNN_GridSearchCV' } , 
                                  ignore_index = True)
print(model_final)

    metrics  values             model
0  accuracy  0.9419             kNN_k
1  accuracy  0.9513  kNN_GridSearchCV

Optimal model and final thoughts

• We can use the model we just built to predict on the test set
• It is optimized because of the cross-validation and will use the optimized k
• Although it is an optimized model, it does not mean it will perform better than our previous model, it is just more accurate

kNN_best = grid.best_estimator_

# Check accuracy of our model on the test data.
print(kNN_best.score(X_test, y_test))

0.9458577951728636

kNN_champ = kNN_best.score(X_test, y_test)

Pickle - what?

Final Model

• Let’s save our final model in case we need to use it another time.

# Save this final model
model_final = {'metrics' : "accuracy" , 
                                  'values' : round(kNN_champ, 4),
                                  'model':'kNN_optimized' }
print(model_final)

{'metrics': 'accuracy', 'values': 0.9459, 'model': 'kNN_optimized'}

pickle.dump(model_final, open(str(data_dir)+"/"+"model_final.sav", 'wb'))

Module completion checklist

kNN pros and cons

Knowledge check

Exercise

Module completion checklist

kNN : Topic summary

In this part of the course, we have covered:

• Supervised learning and its use cases
• The theory behind kNN algorithm
• Implementation of kNN on a dataset
• Performance optimization for kNN

Congratulations on completing this module!